The correct answer is C. The DATA step and PROC PRINT steps ran without errors. In the SAS log

shown, there is a clear error in the PROC SORT step because of the use of an incorrect option by

ascending Increase which caused the "Variable ASCENDING not found" error. However, the DATA step

completed successfully, as indicated by the "NOTE" that follows it, confirming that 56 observations

were read from the SASHELP.SHOES data set where Region is Africa. Additionally, the PROC PRINT

step also completed successfully, indicated by the "NOTE" at the bottom of the log which confirms

that 56 observations were read from the WORK.AFRICA data set, implying the PROC PRINT step

executed without issue.

A is incorrect because the error in PROC SORT did not cause the program to stop; SAS continued

processing the next steps. B is incorrect because the PROC SORT step did not run successfully. D is

incorrect because the PROC PRINT step did not fail; it ran successfully.

Reference:

SAS documentation on error messages.

Understanding the SAS log and error messages.

Data set options in SAS, such as KEEP= (to specify variables to keep in a dataset) and RENAME= (to

change the names of variables in a dataset), have specific rules for their usage:

A . The options must be placed in parentheses - This is correct. When specifying dataset options in

SAS, they must be enclosed in parentheses immediately following the dataset name, without any

spaces in between.

D . The options are placed after the data set name - This is also correct. Dataset options follow the

dataset name and are enclosed in parentheses to modify how SAS reads or writes the dataset in that

step.

Option B is incorrect because data set options are allowed in both DATA and PROC steps, provided

they are relevant to the procedure being used. Option C is incorrect because data set options can be

applied to both input and output datasets, depending on the context and specific operation being

performed.

Reference: SAS 9.4 Language Reference: Concepts.

The reason the labels for MSRP, MPG_City, and MPG_Highway are not displaying in the PROC PRINT

report is that the LABEL option was not used in the PROC PRINT statement. In SAS, even if you have

label statements in your DATA step, you must also specify the LABEL option in the PROC PRINT

statement for those labels to be used in the report output. Thus, the correct answer is Option A: You

must use the LABEL option on the PROC PRINT statement.

Reference:

SAS documentation on the PROC PRINT statement and LABEL option, SAS Institute.

The PROC MEANS report shown in the image displays statistics for a single variable Age with no

decimal places. The correct SAS code to generate such a report is option D, which uses the PROC

MEANS procedure with the maxdec=0 option to control the number of decimal places displayed (in

this case, zero) and specifies Age as the analysis variable using the var statement.

The PROC MEANS procedure computes descriptive statistics for numeric variables. The maxdec=0

option is used here to remove decimal places from the report output, which matches the output

shown where the mean, standard deviation, minimum, and maximum are all integers.

The other options are incorrect for the following reasons:

A uses the class statement, which is not appropriate here because Age is not a classification variable

but an analysis variable.

B uses the group statement, which is not a valid statement in the PROC MEANS procedure.

C uses the by statement, which requires the data to be sorted by the BY variable and does not fit the

output provided since it would produce separate statistics for each value of Age.

Reference:

SAS 9.4 documentation for the PROC MEANS statement: SAS Help Center: PROC MEANS

The correct answer is C. To create the NODONATIONS dataset, which includes only the employees

who did not make any donations, a merge operation is used with a conditional statement to filter out

those records. The syntax if inE=1 and inD=0; ensures that the merged dataset includes only the

observations from the EMPLOYEES dataset that do not have a corresponding observation in the

DONATIONS dataset. This is achieved using data step processing with the in= option to create

temporary variables that indicate whether the data is coming from the input dataset.

In the other options:

A is incorrect because inE=0 and inD=0 will never be true after a merge since at least one of the

datasets must contribute to the merged observation.

B lacks the necessary conditional logic to filter out employees with donations.

D is incorrect because if inE=1 and inD=1; would actually select employees who made donations, the

opposite of what we want.

Reference:

SAS documentation on merging data sets using the DATA step.

Option B is correct. In SAS, variable names must be 1 to 32 characters in length. The other

statements about variable names are incorrect:

A is incorrect because variable names are not case sensitive in SAS.

C is incorrect because variable names are required when you create a data set; each column must

have a unique name.

D is incorrect because variable names cannot start with a number, though they can start with an

underscore or a letter.

Reference:

SAS 9.4 documentation on SAS variables.

In the provided DATA step, the merge statement is used to combine the PATIENT and VISIT data sets

by the variable Id. The number of observations in the resulting ALLVISITS data set will equal the

number of unique Id values that appear in both the PATIENT and VISIT data sets, because the merge

in SAS is a one-to-one merge by default when the by statement is used without additional options

like in=.

Looking at the provided data sets, each Id in the PATIENT data set has corresponding entries in the

VISIT data set. Since there are 5 unique Id values and the VISIT data set contains multiple

observations for some Id values (specifically, Id 2 and 5 have more than one visit), the ALLVISITS data

set will have a total of 7 observations (1 for each patient plus the additional visits for Id 2 and 5).

Reference:

SAS documentation on the merge statement.

The correct syntax for the CONTENTS procedure is to specify the dataset to be described using the

data= option. The dataset should be identified by its library and name, separated by a period. The

correct syntax is therefore Proc contents data=library.dataset;, which makes Option C the correct

answer: Proc contents data=sashelp.shoes;. This statement will produce the contents of the dataset

'shoes' in the 'sashelp' library.

Reference:

SAS documentation on the CONTENTS procedure, SAS Institute.

In the DATA step provided, average is calculated using the mean function, which calculates the average of the non-missing values of the variables listed. The variables provided are: var1 = 2 var2 = 4 var3 = . var4 = 6 The mean function in SAS ignores missing values, represented by a period (.). Hence, the average is computed using the non-missing values (2, 4, and 6). The mean of these three numbers is calculated as: (2 + 4 + 6) / 3 = 12 / 3 = 4 Therefore, the value of average is 4. Reference: SAS documentation on the MEAN function, SAS Institute.

In SAS, the DATA step is a powerful tool that allows programmers to perform a variety of tasks such as reporting, managing, and analyzing dat a. The DATA step processes data one observation at a time, making it highly efficient for data manipulation tasks. It enables the creation of new datasets, modification of existing ones, and complex data transformations. Additionally, within a DATA step, you can use a wide range of programming statements and functions to calculate new variables, merge or sort datasets, and perform conditional processing. The flexibility and functionality provided by DATA steps make them a fundamental part of SAS programming for handling and preparing data for further analysis or reporting. Reference: SAS 9.4 Language Reference: Concepts.